The cryoelectro​​​n microscopy (cryo-EM) facility, established in 2016, counts with state-of-the-art equipment for preparation and imaging of vitrified biological specimens. The aim of the facility is to provide state-of-the-art equipment and know-how for determination of high-resolution three-dimensional macromolecular structures.

The centerpiece of the facility is a 200 kV FEI Talos Arctica X-FEG transmission electron microscope (TEM) outfitted with an Autoloader specimen handling system, a CETA CMOS camera and a Gatan K2 direct electron detector (DED; to be upgraded to the K3 model in the spring of 2018). The facility also counts with a new 120kV Talos L120C TEM for low-resolution work in stain and cryo and for cryo-sample screening. A Gatan Solarus 950 plasma cleaning system, an FEI Vitrobot Mark IV, glow discharge and carbon evaporation systems, and all other ancillary equipment required for cutting-edge cryo-EM work are available. Cryo-EM data collection is fully automated using the Leginon system. All TEM image data is directed to a web-accessible database that makes possible remote monitoring of data collection and on-the-fly preliminary image analysis (DED frame alignment, image evaluation, particle picking, etc.) using Appion.

The facility is located in the heart of the Anschutz Medical School Campus, on the ground level of the RC-1 South Building (Rm 1301B). It is directed by Francisco Asturias and is managed by Peter Van Blerkom, both in the Department of Biochemistry & Molecular Genetics.​​

*Users are billed at Rate 1 for the first 24 hours of Arctica use in a month. Users are billed at Rate 2 after 24 hours.

**Users are billed at Rate 1 for the first 6 hours of L120C use in a month. Users are billed at Rate 2 after 6 hours.

***Data analysis is only offered where time is available

Any projects where Anschutz CryoEM staff prepare samples, grids, or do analysis of resulting images are considered to be collaborations. For collaboration work with the CryoEM facility that results in publication, CryoEM staff members should be listed as co-authors.

For data collection-only services, on user-provided ready-to-image cryo grids, if CryoEM staff enabled customization of approaches or made significant intellectual input or overcame extra difficulties (such as multiple loads of grids to find a usable one instead of a single load of up to 8 grids for one sample) to make the work possible, they should be listed as co-authors. Otherwise, the following acknowledgement should be included in the publication:

1. When describing data collection in the Materials and Methods or equivalent section, please mention that data was collected on a 200 kV FEI Talos Arctica microscope or a 120 kV FEI Talos L120C located at the Anschutz Medical Campus.

2. In the Acknowledgement section of the paper, please acknowledge help from the Anschutz CryoEM facility staff.

After the paper is published, please send a link to the paper as well as one representative figure to the director of the Anschutz CryoEM facility.​

Billing rates subject to change

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EMAN2: A broadly based greyscale scientific image processing suite with a primary focus on processing data from transmission electron microscopes. EMAN's original purpose was performing single particle reconstructions (3-D volumetric models from 2-D cryo-EM images) at the highest possible resolution, but the suite now also offers support for single particle cryo-ET, and tools useful in many other subdisciplines such as helical reconstruction, 2-D crystallography and whole-cell tomography. EMAN2 is capable of processing very large data sets (>100,000 particle) very efficiently (up to 20x faster than EMAN1).

RELION: RELION (for REgularised LIkelihood OptimisatioN, pronounce rely-on) is a stand-alone computer program that employs an empirical Bayesian approach to refinement of (multiple) 3D reconstructions or 2D class averages in electron cryo-microscopy (cryo-EM). It is developed in the group of Sjors Scheresat the MRC Laboratory of Molecular Biology. Briefly, the ill-posed problem of 3D-reconstruction is regularised by incorporating prior knowledge: the fact that macromolecular structures are smooth, i.e. they have limited power in the Fourier domain. In the corresponding Bayesian framework, many parameters of a statistical model are learned from the data, which leads to objective and high-quality results without the need for user expertise. The underlying theory is given in Scheres (2012) JMB. A more detailed description of its implementation is given in Scheres (2012) JSB. If RELION is useful in your work, please cite at least one of these papers.​